What is the difference between machine scoring systems and chatbots?
The term Artificial Intelligence (AI) has become a buzzword used in various businesses, organizations, and media in general. Many have warned about the dangers of using such a phrase as a blanket term to describe technologies that are not truly AI, as it tends to mislead the public about what AI is and what their expectations should be (The AI Buzzword Trap, n.d.). In the same vein, there has been a trend to equate AI to chatbots like ChatGPT. This is not uncommon even among academics (Jordan, 2019). In a recent Applied Linguistics academic conference, there were a total of 40 presentations related to the search term “Artificial Intelligence” out of which about 31 were about generative AI or chatbots, like ChatGPT.
AI is much bigger than chatbots. In fact, AI encompasses a variety of technologies that enable machines to perform tasks requiring human-like intelligence. Self-driving cars are a real-world example of AI technology that is beyond chatbots. Just like when training a human to drive safely, the machine is trained to recognize traffic signs, avoid obstacles, make decisions at intersections, and overall follow the traffic regulations. With the help of (1) sensors that gather millions of data points on what is ahead, beside, or behind, (2) software that processes all these data points collected through the sensors, and (3) machine learning that recognizes patterns in the data points collected to support the machine in improving their driving, a machine is able to perform the human-like task of driving a car in real traffic.
Likewise, ACTFL® and Language Testing International® (LTI) have leveraged state-of-the-art machine learning technologies to build a model that would provide scores to Spanish AAPPL PW (ACTFL Assessment of Performance toward Proficiency in Languages Presentational Writing) responses just like ACTFL certified raters would do. Like with self-driving cars, the research team at ACTFL and LTI trained the machine to perform the task of a certified human rater by (1) compiling thousands of data points of actual test responses and rater scores, (2) using software to process these data, and (3) applying machine learning techniques to find patterns to optimize the machine scoring performance.
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